Falling is a significant problem in the care of the elderly that can lead to morbidity and mortality. From a physical perspective, falls causes injuries, while from the mental perspective, falls causes fear-of-falling, which in turn leads to social isolation and depression.
In terms of intervention, there are two aspects where electronic devices can assist. One is to provide an automated and reliable fall detection system, and the other is to provide a fall prevention system that provides early feedback to the user or the user's care provider if the user engages in a (more) risky situation. The first assures adequate measures will be taken in case of a fall incident, which also provides a level of reassurance to the user, and the second assists the user in staying healthy, which provides a further level of reassurance. Fall detection systems are becoming widely available, and fall prevention systems are expected to appear shortly.
Commonly, automated fall detection systems are centered around an accelerometer which is to be attached to the user's body. The detector tracks the signals from the accelerometer and determines that a fall has taken place if a characteristic pattern is identified. A typical pattern is a combination of a high impact value in which the acceleration signal exceeds a preconfigured threshold, followed by a period of relatively constant acceleration, for example gravity only, since the user is lying motionless on the ground. The pattern may continue by revealing activity, deviating from the relatively constant period, when the user stands up again.
Several refinements and extensions exist to this simple system. For example, gyroscopes and/or magnetometers can be used to measure the body's orientation to check for a sustained non-vertical position in evaluating whether a fall has occurred.
Current automatic fall detection systems are typically equipped with an “alarm-reset” button that the user can press to suppress false alarms (false positives—FP) before they reach a care provider, so that further intervention by the care provider is aborted. Often, the alarm-reset button, or alternatively an “alarm” button, is used to enable the user to request assistance, which, in a way, indicates a missed alarm (i.e. a false negative—FN). These two functions can appear as two separate buttons for the user to press. They can also be integrated in one physical button, in which case the function switches with the current state of the detection algorithm (no-fall versus fall detected). It should be noted however that the buttons are not required to be part of the device attached to the user's body. They could also be part of a base station, located in the home of the user, to which the sensor communicates and which further transmits an alarm to the care provider's call centre. It makes most sense to mount the button for the reset function on the base station and to have the alarm function with the sensor.
One problem with automatic fall detection systems is the reliable classification of falls and non-falls, characterized through sensitivity and specificity. Clearly, for reliable classification, false positives and false negatives should be suppressed as much as possible. Full reliability (i.e. no FP or FN) is only achievable if the characteristics of the signal feature set can be distinguished completely in two separate sets, one characterizing a fall incident, the other a non-fall incident. Obviously, in fall prevention, the system cannot make use of the high acceleration events in the signal, since they will not (yet) be present, and the problem of correct identification of increased risk situations is even more difficult.
Many techniques to arrive at correct classifications are known. They are collectively referred to as machine learning [T. M. Mitchell, Machine Learning, McGraw-Hill, 1997]. In these methods, an algorithm is designed that classifies value combinations of features from the sensor signals as characterizing a fall or a non-fall. Using feature sets that are known to correspond to a fall or non-fall, the algorithm's parameters are adapted to provide a correct response to this training data. The amount of adaptation is usually derived from a statistical analysis of the algorithm, so that the update process converges to a situation that matches an optimality criterion. Of course, in order to be perfectly successful, it is required that the signals, i.e. their observed features, are distinguishable in the ideal, i.e. noise-free, situation. If this is not the case, errors (FP and FN) will fundamentally remain, and the task is to find an optimal setting trading these FP and FN. For an effective training of the algorithm, a sufficient amount of data samples are needed, so that the classification boundaries can be optimized for the variance in the feature set.
A problem that remains concerns the acquisition of the reference data so that it is of sufficient size and sufficiently represents the classes to be distinguished. Since people move in different ways, and hence will generate different signals and patterns, it is hard to provide a “one-size-fits-all” set of reference data.
Therefore, it is an object of the invention to provide a fall detection and/or prevention system that can be adapted to a particular user's fall or activity characteristics in order to improve the reliability of the fall detection algorithm, without requiring the user to spend a dedicated period of time training the detector. It is a further object of the invention to provide a fall detection and/or prevention system that can adapt to changes in the user's activity characteristics (for example, due to ageing).